1. Field
The embodiments relate to a method and apparatus for generating time series data sets for predictive analysis.
2. Description of the Related Art
Current attempts to predict future trends in time series data are based on r numerical data or mathematical transformations of that raw data. In some application domains where there is no clear pattern in the time series, such as stock market trend prediction, prediction accuracy is often relatively low.
Many machine learning approaches to time series prediction have been proposed in the past, for example on the basis of regression methods or neural networks. However, these approaches struggle on time series data which lack obvious patterns or trends. One example of such data is stock market time series data. Although many approaches to predicting the direction of stock market change are reported to outperform benchmark models, hit-rates (i.e. the percentage of accurate predictions out of all predictions made by a particular method) of 50% to 65% on test data are still fairly commonplace, the lower end of which range is equivalent to little more than random guessing.
It is desirable to improve the accuracy of time series prediction where patterns or trends in the data cannot easily be recognized.